CN107168285B - A kind of automobile intelligent fault diagnosis and maintenance householder method and system - Google Patents
A kind of automobile intelligent fault diagnosis and maintenance householder method and system Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 171
- 238000003745 diagnosis Methods 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 title claims abstract description 75
- 230000008439 repair process Effects 0.000 claims abstract description 28
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 230000007246 mechanism Effects 0.000 claims description 27
- 230000003993 interaction Effects 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 16
- 238000004092 self-diagnosis Methods 0.000 claims description 13
- 239000011159 matrix material Substances 0.000 claims description 10
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- 208000024891 symptom Diseases 0.000 claims description 9
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- 238000004378 air conditioning Methods 0.000 description 1
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- 238000004891 communication Methods 0.000 description 1
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- 239000000295 fuel oil Substances 0.000 description 1
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- 230000001050 lubricating effect Effects 0.000 description 1
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- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
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Abstract
The present invention provides a kind of automobile intelligent fault diagnosises and maintenance householder method and system, diagnose interface by OBD and obtain the objective raw diagnostic data of automobile；After pre-processing to it, error code is determined whether, if so, then parsing to this error code, and provide auto repair auxiliary for it；If fault-free code, Quantitative yield is carried out by the automobile subjectivity indeterminate fauit sign qualitative value that cloud model method will acquire, and be compared with automotive system standard database Plays value, the automatic tentative diagnosis of system goes out vehicle failure position；Vehicle failure reason is diagnosed to be by the reasoning by cases method based on normal cloud model；And rule-based reasoning auxiliary can be used to complete automobile failure diagnosis and exclude.The present invention solves that automobile is faulty and the complex situations of fault-free code or vehicle failure caused by being damaged by non-electronic components, efficiently, quickly finishes automobile failure diagnosis, specification maintenance step, rational maintenance price.
Description
Technical field
The present invention relates to automobile failure diagnosis and maintenance field, more particularly to a kind of automobile intelligent fault diagnosis and maintenance
Householder method and system.
Background technique
Automobile failure diagnosis and maintenance technique refer in the case where not disintegrating to vehicle, by observing vehicle failure
Sign and automotive service situation is determined using instrument and equipment, failure cause and trouble location are found out, using corresponding method for maintaining
And tool excludes vehicle failure.Since new auto technology makes rapid progress, automobile electric control system is increasingly sophisticated, what automobile broke down
The reason of frequency is higher and higher, and automobile breaks down becomes increasingly complex.After automobile breaks down, search what automobile broke down
The reason occupied time is 70%, and only accounts for 30% to the time that automobile repairs.
The service industry of China's auto repair at present is also in fast-developing primary stage, the whole educational background of maintenance practitioner
Relatively low, the technical staff with automobile failure diagnosis ability only accounts for 20% or so, and supervisory system be not also it is very perfect, cause very
There are unreasonable, opacity in the maintenance price of automobile for more vehicle maintenance and repair enterprises, and the maintenance flow of automobile is not advised
Model has seriously affected customer for the satisfaction of these enterprises services, and the more impression of customer are " dirty, unrest, difference ".
When vehicle-mounted self-diagnosis system (OBD) can be according to some component malfunction of automobile, formed in automobile ECU corresponding
Error code, by application corresponding fault detection system for automobile read automobile ECU in fault code, then pass through error code
Table interprets acquired error code, so that it is determined that the reason of automobile breaks down.However OBD primarily focuses on monitoring car engine
Machine control system and exhaust system when breaking down to the automobile other systems other than this, will not store failure in automobile ECU
Code, therefore it is unfavorable for the exclusion of vehicle failure, and fault detection system for automobile currently used in the market is with code reader mostly
Based on hand-held diagnostic equipment, using OBD diagnosis interface In-vehicle networking is diagnosed, the results are shown on screen.Its
Be primarily present following defect: 1. diagnostic instrumentation systems are fixed, and system extension upgrading or program change are relatively difficult；2. can not handle
Automobile necessary being failure and in automobile ECU the case where fault-free code；3. the automobile caused by non-electronic components can not be handled therefore
Barrier；4. specific failure need to be pointed out by servicing manual, it cannot provide and intelligentized diagnose help；5. cannot provide comprehensively reliable
Historical failure diagnostic data can not save maintenance technician and search fault time.
Summary of the invention
The purpose of the present invention is be quickly found out the reason of causing automobile to break down, solve that general vehicle failure can not be used
Diagnostic device handles automobile necessary being failure and fault-free code and because automobile caused by non-electronic components damage breaks down
The case where, for the insufficient maintenance personal of experience provide detection and diagnosis the step of and rational maintenance price, shorten automobile
The time of fault diagnosis and maintenance improves the quality of auto repair service.
Technical solution of the present invention:
A kind of the automobile intelligent fault diagnosis and repair assisting system of combination subjective and objective information and cloud model, including OBD are examined
Slave interrupt interface module, automobile objective diagnosis initial data obtain module, data preprocessing module, error code parsing module, cloud model
Data processing module, fault self-diagnosis module, the fault case reasoning module based on cloud model, Process Based module and dimension
Repair supplementary module；
The OBD diagnosis interface module is for connecting automobile ECU and automobile intelligent fault diagnosis of the invention and maintenance
Auxiliary system；
The automobile objective diagnosis initial data obtains module and connect with OBD diagnosis interface module, with service request
Mode asks for vehicle diagnosis initial data to automobile ECU, and automobile ECU is in a manner of service response to automobile objective diagnosis original number
Vehicle diagnosis initial data is transmitted according to module is obtained；
The data preprocessing module obtains module with automobile objective diagnosis initial data and connect, to acquired automobile
Diagnostic raw data is handled, and the error code in vehicle diagnosis initial data is separated with other fault diagnosis datas,
By treated, data classification is stored into data prediction volatile data base；
The error code parsing module is connect with data preprocessing module, will be stored in data prediction volatile data base
Error code compared with failure code table, be matched to fault message corresponding with corresponding failure code, so that it is determined that automobile occur
The reason of failure；
The cloud model data processing module by client to trouble vehicle the phenomenon that description and maintenance personal briefly extract
Vehicle failure sign fuzzy payoff be converted into quantitative value；
The fault self-diagnosis module is connect with data preprocessing module and cloud model data processing module respectively, automatically
Tentative diagnosis goes out vehicle failure position, including automotive system standard database and automatic fault analysis mechanism；
The fault case reasoning module connecting fault self diagnosis module based on cloud model, at the beginning of fault self-diagnosis module
After step is diagnosed to be vehicle failure position, vehicle failure case symptom attribute value is extracted in detail for the trouble location, inputs base
Fault case reasoning is carried out in the fault case reasoning module of cloud model, determines the concrete reason that automobile breaks down, including people
Machine interactive module, fault case reasoning module and fault case study module；
The Process Based module carries out failure to automobile by the vehicle inspection and maintenance step concluded, summarized
Diagnosis and exclusion, including human-computer interaction module and rule-based reasoning module；
The maintenance supplementary module is used for connecting fault code parsing module or the fault case reasoning mould based on cloud model
Block, by error code parsing module or after the fault case reasoning module of cloud model makes a definite diagnosis the reason of automobile breaks down,
The information such as required maintenance time, maintenance price are provided for client by maintenance supplementary module, standardize auto repair process, packet
Include human-computer interaction module, Maintenance Cases reasoning module and Maintenance Cases study module.
A kind of the automobile intelligent fault diagnosis and maintenance householder method of combination subjective and objective information and cloud model, steps are as follows:
Step 1: it is auxiliary by automobile ECU and automobile intelligent fault diagnosis of the invention and maintenance to diagnose interface module by OBD
Auxiliary system is attached；
Step 2: obtaining automobile objective diagnosis initial data, interface module is diagnosed by OBD, in a manner of service request
Vehicle diagnosis initial data is asked for automobile ECU, automobile ECU is obtained in a manner of service response to automobile objective diagnosis initial data
Modulus block transmits vehicle diagnosis initial data；
Step 3: data prediction, by the error code and other fault diagnosises in vehicle diagnosis initial data obtained
Data are separated, and by treated, data classification is stored into data prediction volatile data base.
Step 4: in judgement data after treatment whether faulty code；
Step 5: parsing error code, the error code stored in data prediction volatile data base and failure code table are carried out
Comparison, is matched to fault message corresponding with corresponding failure code, determines failure cause；
Step 6: the phenomenon that record client is to trouble vehicle description information；
Step 7: maintenance personal briefly extracts vehicle failure sign fuzzy payoff；
Step 8: cloud model data processing, by applying cloud model, by client to the phenomenon of the failure description information of automobile and
The vehicle failure sign fuzzy payoff that maintenance personal briefly extracts is converted into the quantitative values of digital representation；
Further, the cloud model, the method for applying expert statistics first, obtains the desired value Ex of Fog property:
In formula, Ex is Fog property desired value；XiFor perception extraction；N is attribute value quantity；
After obtaining the desired value of Fog property, entropy En is calculated:
In formula, En is Fog property entropy；
The entropy of entropy is calculated according to desired value and entropy, i.e. the super entropy He of Fog property:
In formula, He is the super entropy of Fog property；
Finally obtain the membership clouds expectation curve μ of normal distribution:
μ=exp (- (x-Ex)2/(2En2)) (4)
In formula, μ is the membership clouds degree of membership of normal distribution；
Quantitative numerical value is converted by subjective uncertainty vehicle failure sign qualitative value according to curve μ；
Step 9: fault self-diagnosis module includes automotive system standard database and automatic fault analysis mechanism；
Automotive system standard database: it is used to each mechanism of classification storage automobile or system important attribute standard value；
Automatic fault analysis mechanism: for analyzing the current automobile failure diagnosis value briefly obtained and automotive system criterion numeral
According to the difference between the standard value stored in library；
Further, to each mechanism of automobile or system important attribute standard value stored in automotive system standard database,
There is the abnormal percentage contribution difference to break down to mechanism or system according to each attribute value, distributes suitable attribute weight；Therefore
Barrier automatically analyzes mechanism and compares current automobile failure diagnosis value with standard value, if difference between the two is beyond permission
It is abnormal then to judge that this attribute value occurs for range, and so on, until having judged all properties, finally to there is abnormal attribute institute
The weight summation accounted for, if summation is more than set threshold value, system tentatively judges which mechanism or system break down automatically；
Step 10: the fault case reasoning fault diagnosis based on cloud model, passes through the automobile fuzzy fault attribute that will be obtained
It is converted into quantitative value using the method for cloud model, use case inference technology is diagnosed to be the reason of automobile breaks down, including
Human-computer interaction module, fault case reasoning module and fault case study module；
The human-computer interaction module based on cloud model: the typing of vehicle failure sign value is realized；
The fault case reasoning module connects human-computer interaction module, indicates including fault case, fault case library
Tissue and fault case retrieval；
The fault case indicates: arranging to the historical failure case of collection, extracts the spy of historical failure case
Sign, is stored in fault case database, and fault case indicates the case representation method of a five-tuple in this method
To indicate vehicle failure case:
E=<N, T, D, R, A>(5)
In formula, N is fault case unique identifier；T is the affiliated type of fault case, T={ T1, T2..., Tn}；D is event
Hinder case symptom attribute value, D={ D1, D2..., Dn}；R is vehicle failure reason；A is the aid illustration information of fault case, A
={ A1, A2..., An}。
The tissue in the fault case library by the fault case of collection by formed automobile mechanism or system it is different into
Row classification storage；Fault case under each mechanism or system is divided into the two-stage case layer of upper layer case library Yu lower layer's case library
Secondary structure；
The upper layer case library be abstracted be in lower layer's case all cases in lower layer's case library feature represent；It chooses
Principle is upper layer case to the distance of each lower layer's case and most short；
Lower layer's case library is the concrete case of upper layer case, is clustered by using the shortest distance under hierarchical method
Algorithm classifies to history case by principle of similarity, and similar case is then classified as one kind；
The shortest distance clustering algorithm sets SIM (xi, xj) indicate sample fault case xiAnd xjBetween similarity,
Cluster CmAnd CnBetween similarity be similarity in two clusters between most like case, indicated with following formula (6):
In formula, SijFor cluster CmAnd CnBetween similarity；SIM(xi, xj) it is sample fault case xiAnd xjBetween it is similar
Degree；xiFor the sample fault case of serial number i；xjFor the sample fault case of serial number j；CmFor the cluster of serial number m；CnFor sequence
Number be n cluster；
If cluster CrFor cluster CmAnd CnNew cluster made of after merging, then either cluster CaWith CrBetween similarity are as follows:
In formula, SarFor either cluster CaWith CrBetween similarity；CrFor cluster CmAnd CnNew cluster made of merging；
Specific step is as follows for algorithm:
(1) similarity of sample fault case between any two is calculated, the result being calculated is expressed as a similarity
Matrix is denoted as S (0), at first, each sample fault case self-contained cluster, and shown in similarity matrix such as following formula (8):
(2) greatest member for being greater than given threshold η on off-diagonal is found out from similarity matrix S (0), by CmWith CnIt closes
And at a new cluster, it is denoted as Cr, i.e. Cr={ Cm, Cn}；
(3) the calculating formula of similarity S by being provided in formula (7)ar=max { Sam, SarCalculate new cluster CrWith other clusters Ca
Between similarity, m, n row in S (0) and m, n column are merged into a newline and newly arranged, newline made of merging newly arranges note
For cluster Cr, it is formed by new matrix and is denoted as S (1)；
(4) S (2) are obtained to S (1) (2) (3) two step that repeats the above steps, circuited sequentially, until in similarity matrix S (k)
The greatest member of off-diagonal is less than given threshold η；
The fault case retrieval is made using Technique Using Both Text case Similarity matching method and the nearest neighbor method based on cloud model
Similarity is more than given threshold by the similarity that current failure case Yu historical failure case are calculated for the searching algorithm of case
Fault case return to user, for solving current vehicle failure；
This method will be in the fault case symptom attribute of fault case, car category, brand, vehicle, automobile type configuration, traveling
Journey, purchase vehicle date property as fault case search condition, wherein car category, brand, vehicle, there are layers for automobile type configuration four
The attribute of secondary relationship calculates local similarity as first part, using semantic case Similarity matching method；Remaining failure case
Example attribute is then used as second part to calculate similarity using the nearest neighbor method based on cloud model；
The semantic case Similarity matching method is according to node depth weight density monotonically increasing function:
In formula, DepIt (X) is the depth value of nodes X；X is node；α1For the intermediate number of plies, byCapping integer；
And node density weight model:
In formula, Des(c) density value for being node c；Deg(c) out-degree for being node c；
Obtain the local similarity between case attribute:
In formula, S (X, T)OfficeFor case attribute local similarity；ciFor node i；α2For nodes X depth and inverse,
The nearest neighbor method based on cloud model provides the similarity formula between case attribute:
In formula, SIM (X, T) is case attribute local similarity；DIST (X, T) is the distance between case attribute；WjFor j
Attribute Weight weight values；XijFor j-th of attribute value of i-th of history case；TjFor j-th of attribute value of present case；
Similarity between above formula (12) computation attribute is then directly passed through for the fault attribute value of quantitative value；For fixed
Property fuzzy fault attribute, then calculate the desired value Ex, entropy En, super entropy He of qualitative Fog property according to formula (1) (2) (3) (4)
With the membership clouds expectation curve μ of normal distribution, the membership clouds expectation curve by normal distribution shown in application following formula (13) is close
Seemingly represent " water dust " in cloud model, a cloud model
In formula, y is the membership clouds degree of membership of normal distribution；
Expectation curve represents a qualitative fuzzy fault attribute value, and the expectation curve by solving two cloud models intersects weight
The area of part is folded to indicate the similarity of two qualitative fuzzy fault attributes.And finally to calculated qualitative fault attribute phase
Like degree and the summation of qualitative fuzzy fault attributes similarity, SIM (X, T) is obtainedOffice.Final total similarity between case are as follows:
S(X,T)Always=0.2*S (X, T)Office+0.8*SIM(X,T)Office (14)
In formula, S (X, T)AlwaysFor total similarity of case；
The fault case study module is that new case is added in fault case library, by newly added fault case and history
Fault case compares, if the historical failure case similarity in newly added fault case and case library is less than setting
Newly added fault case is then saved in fault case library by threshold value, if similarity has been more than the threshold value of setting, is illustrated new
The value that the fault case of addition does not need to save abandons the addition of new fault case；
Step 11: judging after carrying out automobile failure diagnosis by the fault case reasoning based on cloud model, vehicle failure
Whether reason is made a definite diagnosis；
Step 12: Process Based automobile failure diagnosis, passes through the vehicle inspection and maintenance step pair concluded, summarized
Automobile carries out fault diagnosis and excludes, including human-computer interaction module and rule-based reasoning module；
The human-computer interaction module of the Process Based provides fault diagnosis and maintenance Step Information for user；
The rule-based reasoning module connects human-computer interaction module, including rule base and forward reasoning unit；
The rule base is stored respectively by application failure maintenance step table and trouble hunting rule list from practical experience
Obtained in specific rules；
The troubleshooting step table includes the information such as number of steps, step title, step details, remarks.
The trouble hunting rule list includes number of steps, inspection result, the in next step information such as number, remarks.
The forward reasoning unit uses Wether (condition judgement) Yes or No (result) format, judges current inspection
Whether survey condition is true, if so, then enter next detecting step corresponding with Yes, if not, then enter opposite with No
Another detecting step answered, recycles according to this, until completing current automobile failure diagnosis；
Step 13: maintenance auxiliary: true by parsing error code or the fault case reasoning fault diagnosis based on cloud model
It after examining the reason of automobile breaks down, is assisted by using maintenance, step, maintenance tool, maintenance price, maintenance time will be repaired
Etc. information return to user, provide the vehicle failure maintenance flow of standard for maintenance personal, for client provide standard automobile dimension
Repair price, including human-computer interaction module, Maintenance Cases reasoning module and Maintenance Cases study module；
The human-computer interaction module of the described maintenance auxiliary connects Maintenance Cases reasoning module, realize user to failure cause and
The typing of the maintenances such as maintenance mode retrieval information；
The Maintenance Cases reasoning module includes the expression of Maintenance Cases and the retrieval of Maintenance Cases；
The expression of the Maintenance Cases indicates auto repair case with a four-tuple:
C=<I, G, S, L>(15)
In formula, I is Maintenance Cases unique identifier；G is maintenance tool, G={ G1, G2..., Gn}；S is maintenance solution party
Case；L is the aid illustration information of Maintenance Cases, L={ L1, L2..., Ln}；
The Maintenance Cases are retrieved the car category in Maintenance Cases attribute, brand, vehicle, automobile type configuration, maintenance
Mode, purchase car fare lattice, purchase vehicle date etc. attributes are as Maintenance Cases search condition, wherein car category, brand, vehicle, vehicle
There are the semantic cases that the attribute of hierarchical relationship uses fault case search method as above to use as first part for configuration four
Example Similarity matching method calculates local similarity；Remaining Maintenance Cases attribute is then used as second part using shown in above formula (12)
Nearest neighbor method calculates the similarity between Maintenance Cases.Most like Maintenance Cases are retrieved from Maintenance Cases library, by its institute
It needs maintenance tool, maintenance step, maintenance price, maintenance time etc. to return to maintenance personal or client, provides reference for it.
The maintenance mode attribute is divided to two kinds of values, that is, replaces and repair；According to Experts consultation method in advance by maintenance mode
When attribute is replacement, it is set as 0.5, to be set as 0 when maintenance.
The Maintenance Cases study module is stored part useful in new Maintenance Cases using the mode of learning of increment type
Into Maintenance Cases library, itself case library is enriched.
Beneficial effects of the present invention:
1. carrying out automobile failure diagnosis by using the reasoning by cases method based on cloud model and excluding, it is able to solve automobile
Fault-free code in ECU, and the complex situations of automobile necessary being failure.
2. being answered for vehicle failure caused by being damaged by non-electronic components by extracting the subjective and objective failure symptom value of automobile
With the diagnostic method of fault reasoning, it can efficiently, be quickly diagnosed to be vehicle failure reason, save the auto repair time, improved
Systematic difference range.
3. the comprehensive method using fault case inference method and rule-based reasoning based on cloud model, improves and examines using single
The failure rate of disconnected method, for experience, insufficient maintenance personal provides the standard step of diagnosis and detection.
4. auto repair process is standardized by application maintenance auxiliary, the time required to providing auto repair for client and thoroughly
The information such as bright maintenance price promote the satisfaction of client.
Detailed description of the invention
Fig. 1 is vehicle failure case organization structure chart of the present invention.
Fig. 2 be a kind of combination subjective and objective information and cloud model of the present invention automobile intelligent fault diagnosis and maintenance it is auxiliary
The flow chart of aid method and system.
Fig. 3 is data prediction flow chart of the present invention.
Fig. 4 is the preliminary self diagnosis flow chart of the failure of the present invention based on cloud model.
Fig. 5 is the fault case reasoning automobile failure diagnosis flow chart of the present invention based on cloud model.
Fig. 6 is Process Based automobile failure diagnosis of the present invention and maintenance flow figure.
Fig. 7 is maintenance assisting workflows figure of the present invention.
Specific embodiment
Below in conjunction with attached drawing and technical solution, a specific embodiment of the invention is further illustrated.
Embodiment
Referring to Fig.1, automobile is divided into four parts: chassis, engine, electric appliance and electronic equipment, vehicle body by the present invention.
Chassis includes but is not limited to: steering system, transmission system, driving system, braking system.
Engine includes but is not limited to: crank link mechanism, valve actuating mechanism, fuel oil supply system, ignition system, cooling system
System, lubricating system, air inlet system and exhaust system, starting system.
Electric appliance and electronic equipment include but is not limited to: illumination and signal system, air-conditioning system, instrument and information system, shadow
Sound navigation system, electricity generation system.
Vehicle body includes but is not limited to: car door, vehicle body axle housing, part device, seat, vehicle window inside and outside vehicle body.
Vehicle failure case library is divided into the double-layer structure tissue of upper layer case library and lower layer's case library by the present invention.
S200 is entered step when carrying out automobile failure diagnosis and maintenance referring to Fig. 2, is started.
Step S201: diagnosing interface module by OBD and connect this system with automobile ECU, establish this system and automobile it
Between communication.
Step S202: data needed for carrying out automobile failure diagnosis are obtained.
Step S203: by being pre-processed to the vehicle diagnosis data of acquisition.
Step S204: judge in acquired data whether faulty code.If so, S205 is entered step, if it is not, then
Enter step S210.
Step S205: by comparing the failure code table of the error code of acquisition and standard, determine that automobile breaks down
The reason of.
Step S206: after determining vehicle failure reason, by using maintenance supplementary module, auxiliary maintaining personnel carry out automobile
Breakdown maintenance provides the information such as maintenance price and Maintenance Demand Time for client.
Step S207: client describes the showed phenomenon of the failure of current automobile failure.
Step S208: the current vehicle failure sign of the brief acquisition of maintenance personal.
Step S209: by converting number using the method for cloud model for the automobile fuzzy fault sign value of input system
Value.
Step S210: it treated automobile fuzzy fault sign value and by the pretreated data of step S203 and will deposit
Storage important attribute standard value in automotive system standard database compares.
Step S211: go out the position of automobile failure by automatic fault analysis mechanism tentative diagnosis.
Step S212: detailed extraction vehicle failure sign value.
Step S213: carrying out the fault diagnosis of automobile by using the fault case inference method based on cloud model, wherein
There is the vehicle failure attribute of hierarchical relationship to calculate case office using the method for semantic similarity car category, brand etc.
Portion similarity S (X, T)Office, for Fog property vehicle failure sign value, the part between case is calculated using the method for cloud model
Similarity SIM (X, T)Office, eventually by two local similarities summation you can get it case similarity S (X, T)Always=0.2*S (X,
T)Office+0.8*SIM(X,T)Office.The case that similarity is greater than expert's given threshold is finally retrieved, is solved for solving current vapour
Vehicle failure.
Step S214: judging whether vehicle failure reason is made a definite diagnosis, if then entering step S206, if it is not, then entering step
S215。
Step S215: the diagnosis of vehicle failure is carried out using rule-based reasoning.
Step S216: terminate automobile failure diagnosis and maintenance.
Referring to Fig. 3, detailed process is as follows for data preprocessing module:
Step S300: initial data needed for obtaining automobile failure diagnosis.
Step S301: classification processing is carried out to the initial data of acquisition, error code and other fault diagnosis datas are carried out
Separation.
Step S302: data after treatment are saved in volatile data base.
Referring to Fig. 4, detailed process is as follows for the preliminary self diagnosis of failure based on cloud model:
Step S400: client describes the phenomenon that current automobile breaks down.
Step S401: maintenance personal briefly extracts current vehicle failure sign value.
Step S402: automobile fuzzy fault attribute sign value is handled using cloud model method.
Step S403: the method for applying expert statistics first obtains the desired value of Fog property
Step S404: after obtaining the desired value of attribute, entropy is calculated
Step S405: the entropy of entropy, i.e., super entropy are calculated according to expectation and entropy
Step S406: membership clouds expectation curve μ=exp [- (x-Ex) for normal distribution that finally you can get it2/(2En2)],
Qualitative fuzzy payoff can be converted to quantitative numerical value according to the curve.
Step S407: the standard attribute value of a certain mechanism or system is extracted from automotive system standard database.
Step S408: automotive system standard database stores the standard attribute value of automobile mechanism or system.
Step S409: by automatic fault analysis mechanism by the current vehicle failure sign value of acquisition and automotive system standard
The standard value stored in database compares.
Step S410: judging whether current vehicle failure sign value and standard value error range are greater than preset value,
If so, entering step S411, if it is not, then entering step S407, recycle like this.
Step S411: by previous step comparing result, system tentatively judges that a certain mechanism or system break down automatically.
Referring to Fig. 5, detailed process is as follows for the fault case reasoning automobile failure diagnosis based on cloud model:
Step S500: the fault case reasoning automobile failure diagnosis based on cloud model is initially entered.
Step S501: inspection of the vehicle failure sign value extracted in detail by human-computer interaction interface input as reasoning by cases
Rope condition.
Step S502: there is the fault case attribute of hierarchical relationship to answer car category, brand, vehicle, automobile type configuration etc.
Fault case attribute local similarity S (X, T) is calculated with semantic analogue methodOffice。
Step S503: the qualitative attribute that quantitative attributes application cloud model method is converted into exact numerical is obscured to fault case
Calculate fault case attribute local similarity SIM (X, T)Office。
Step S504: the total similarity S (X, T) of case of being out of order to obtain the final product of summing to the two local similarityAlways=0.2*S (X,
T)Office+0.8*SIM(X,T)Office, opposite by the historical failure case that will be stored in current vehicle failure case and fault case library
Than retrieving similar fault case.
Step S505: judge whether current vehicle failure case and the total similarity of fault case in historical failure case library are big
The preset threshold value of Yu expert, if so, S506 is entered step, if it is not, then entering step S510 and step S511.
Step S506: the similarity that previous step is retrieved is greater than the failure cause of the historical failure case of given threshold
The user is returned, for solving current vehicle failure.
Step S507: after going out the reason of automobile breaks down by the fault case reasoning fault diagnosis based on cloud model,
By using maintenance supplementary module, auxiliary maintaining personnel carry out vehicle failure maintenance, provide maintenance price and maintenance institute for client
The information such as take time.
Step S508: if not retrieving similar fault case, fault case study module is entered, passes through increasing
The mode of learning of amount formula enriches faults itself case library.
Step S509: fault case library stores historical failure case, when carrying out fault case retrieval, from fault case library
Middle extraction historical failure case library.
With reference to Fig. 6, Process Based automobile failure diagnosis and maintenance detailed process are as follows:
Step S600: when carrying out automobile failure diagnosis and maintenance using rule-based reasoning.
Step S601: current symptom of vehicle failure or failure cause are selected.
Whether true step S602: judging current detection condition, if so, S603 is entered step, if it is not, then entering step
S604。
Step S603: according to judging result, into next detecting step.
Step S604: according to judging result, into corresponding another detecting step.
Step S605: judging whether vehicle failure detection is completed, if so, entering step S606, terminates vehicle failure and examines
Disconnected and maintenance, if it is not, then entering step S602, recycles according to this, until completing to detect.
With reference to Fig. 7, auto repair supplementary module detailed process is as follows:
Step S701: vehicle failure reason, Maintenance Cases search condition are inputted by the human-computer interaction interface of maintenance modules
Etc. information.
Step S702: wherein have the Maintenance Cases attribute of hierarchical relationship using semantic phase car category, brand etc.
Maintenance Cases local similarity S (X, T) is calculated like the method for degreeOffice。
Step S703: by maintenance mode attribute be replacement when be set as 0.5, for maintenance when be set as 0, by mileage travelled,
Nearest neighbor algorithm is applied to calculate similarity SIM (X, T) after purchasing the normalization of vehicle dateOffice。
Step S704: the total similarity S (X, T) of Maintenance Cases is shown to the summation of the two local similarityAlways=0.2*S (X,
T)Office+0.8*SIM(X,T)Office, opposite by the history Maintenance Cases that will be stored in current auto repair case and Maintenance Cases library
Than retrieving similar services case.
Step S705: judge whether current auto repair case and the total similarity of Maintenance Cases in history Maintenance Cases library are big
The preset threshold value of Yu expert, if so, S706 is entered step, if it is not, then entering step S707.
Step S706: the similarity that previous step is retrieved is greater than the maintenance step of the history Maintenance Cases of given threshold
Suddenly, the information such as maintenance tool, maintenance price, maintenance time return to user, provide the vehicle failure dimension of standard for maintenance personal
Process is repaired, provides the auto repair price of standard for client.
Step S707: if not retrieving similar Maintenance Cases, Maintenance Cases study module is entered, passes through increasing
The mode of learning of amount formula enriches itself Maintenance Cases library.
Step S708: Maintenance Cases library stores history Maintenance Cases, when repairing Case Retrieval, from Maintenance Cases library
Middle extraction history Maintenance Cases library.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
Change, there is no necessity and possibility to exhaust all the enbodiments, and it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (2)
1. a kind of automobile intelligent fault diagnosis and repair assisting system, which is characterized in that the automobile intelligent fault diagnosis with
Repair assisting system include OBD diagnosis interface module, automobile objective diagnosis initial data obtain module, data preprocessing module,
Error code parsing module, cloud model data processing module, fault self-diagnosis module, the fault case reasoning mould based on cloud model
Block, Process Based module and maintenance supplementary module；
The OBD diagnosis interface module is for connecting automobile ECU and automobile intelligent fault diagnosis and repair assisting system；
The automobile objective diagnosis initial data obtains module and connect with OBD diagnosis interface module, in a manner of service request
Vehicle diagnosis initial data is asked for automobile ECU, automobile ECU is obtained in a manner of service response to automobile objective diagnosis initial data
Modulus block transmits vehicle diagnosis initial data；
The data preprocessing module obtains module with automobile objective diagnosis initial data and connect, to acquired vehicle diagnosis
Initial data is handled, and the error code in vehicle diagnosis initial data is separated with other fault diagnosis datas, will be located
Data classification after reason is stored into data prediction volatile data base；
The error code parsing module is connect with data preprocessing module, the event that will be stored in data prediction volatile data base
Barrier code is compared with failure code table, is matched to fault message corresponding with corresponding failure code, so that it is determined that automobile breaks down
The reason of；
The cloud model data processing module by client to trouble vehicle the phenomenon that the description and vapour that briefly extracts of maintenance personal
Vehicle failure symptom fuzzy payoff is converted into quantitative value；
The fault self-diagnosis module is connect with data preprocessing module and cloud model data processing module respectively, automatic preliminary
It is diagnosed to be vehicle failure position, including automotive system standard database and automatic fault analysis mechanism；
The fault case reasoning module connecting fault self diagnosis module based on cloud model, fault self-diagnosis module are tentatively examined
Behind disconnected vehicle failure position out, vehicle failure case symptom attribute value is extracted in detail for the trouble location, input is based on cloud
The fault case reasoning module of model carries out fault case reasoning, determines the concrete reason that automobile breaks down, including man-machine friendship
Mutual module, fault case reasoning module and fault case study module；
The Process Based module carries out fault diagnosis to automobile by the vehicle inspection and maintenance step concluded, summarized
With exclusion, including human-computer interaction module and rule-based reasoning module；
The maintenance supplementary module is used for connecting fault code parsing module or the fault case reasoning module based on cloud model, leads to
It crosses error code parsing module or after the fault case reasoning module of cloud model makes a definite diagnosis the reason of automobile breaks down, passes through dimension
It repairs supplementary module and provides required maintenance step, maintenance tool, maintenance time, maintenance price information for client, standardize automobile
Maintenance flow, including human-computer interaction module, Maintenance Cases reasoning module and Maintenance Cases study module.
2. a kind of automobile intelligent fault diagnosis and maintenance householder method, which is characterized in that steps are as follows:
Automobile ECU is connected with automobile intelligent fault diagnosis with repair assisting system Step 1: diagnosing interface module by OBD
It connects；
Step 2: obtaining automobile objective diagnosis initial data, interface module is diagnosed by OBD, to vapour in a manner of service request
Vehicle ECU asks for vehicle diagnosis initial data, and automobile ECU obtains mould to automobile objective diagnosis initial data in a manner of service response
Block transmits vehicle diagnosis initial data；
Step 3: data prediction, by the error code and other fault diagnosis datas in vehicle diagnosis initial data obtained
It is separated, by treated, data classification is stored into data prediction volatile data base；
Step 4: in judgement data after treatment whether faulty code；
Step 5: parsing error code, the error code stored in data prediction volatile data base and failure code table are compared,
It is matched to fault message corresponding with corresponding failure code, determines failure cause；
Step 6: the phenomenon that record client is to trouble vehicle description information；
Step 7: maintenance personal briefly extracts vehicle failure sign fuzzy payoff；
Step 8: cloud model data processing, by applying cloud model, by client to the phenomenon of the failure description information of automobile and maintenance
The vehicle failure sign fuzzy payoff that personnel briefly extract is converted into the quantitative values of digital representation；
Further, the cloud model, the method for applying expert statistics first, obtains the desired value Ex of Fog property:
In formula, Ex is Fog property desired value；XiFor perception extraction；N is attribute value quantity；
After obtaining the desired value of Fog property, entropy En is calculated:
In formula, En is Fog property entropy；
The entropy of entropy is calculated according to desired value and entropy, i.e. the super entropy He of Fog property:
In formula, He is the super entropy of Fog property；
Finally obtain the membership clouds expectation curve μ of normal distribution:
μ=exp (- (x-Ex)2/(2En2))(4)
In formula, μ is the membership clouds degree of membership of normal distribution；
Quantitative numerical value is converted by subjective uncertainty vehicle failure sign qualitative value according to curve μ；
Step 9: fault self-diagnosis module includes automotive system standard database and automatic fault analysis mechanism；
Automotive system standard database: it is used to each mechanism of classification storage automobile or system important attribute standard value；
Automatic fault analysis mechanism: for analyzing the current automobile failure diagnosis value briefly obtained and automotive system standard database
Difference between the standard value of middle storage；
Further, to each mechanism of automobile or system important attribute standard value stored in automotive system standard database, according to
There is the abnormal percentage contribution difference to break down to mechanism or system in each attribute value, distributes suitable attribute weight；Failure is certainly
Dynamic analysis mechanisms compare current automobile failure diagnosis value with standard value, if difference between the two is beyond the model allowed
It encloses, then it is abnormal to judge that this attribute value occurs, and so on, until having judged all properties, finally to having shared by abnormal attribute
Weight summation, if summation is more than set threshold value, system tentatively judges which mechanism or system break down automatically；
Step 10: the fault case reasoning fault diagnosis based on cloud model, passes through the automobile fuzzy fault attribute application that will be obtained
The method of cloud model is converted into quantitative value, and use case inference technology is diagnosed to be the reason of automobile breaks down, including man-machine
Interactive module, fault case reasoning module and fault case study module；
The human-computer interaction module based on cloud model: the typing of vehicle failure sign value is realized；
The fault case reasoning module connects human-computer interaction module, indicates including fault case, the tissue in fault case library
And fault case retrieval；
The fault case indicates: the historical failure case of collection arranged, the feature of historical failure case is extracted, it will
It is stored into fault case database, and fault case expression is indicated with the case representation method of a five-tuple in this method
Vehicle failure case:
E=<N, T, D, R, A>(5)
In formula, N is fault case unique identifier；T is the affiliated type of fault case, T={ T1, T2..., Tn}；D is failure case
Example symptom attribute value, D={ D1, D2..., Dn}；R is vehicle failure reason；A is the aid illustration information of fault case, A=
{A1, A2..., An}；
The tissue in the fault case library is divided the fault case of collection by the mechanism of formed automobile or the difference of system
Class storage；Fault case under each mechanism or system is divided into the two-stage case level knot of upper layer case library Yu lower layer's case library
Structure；
The upper layer case library be abstracted be in lower layer's case all cases in lower layer's case library feature represent；Selection principle
For the distance of upper layer case to each lower layer's case and most short；
Lower layer's case library is the concrete case of upper layer case, by using the shortest distance clustering algorithm under hierarchical method
Classify by principle of similarity to history case, similar case is then classified as one kind；The shortest distance clustering algorithm sets SIM
(xi, xj) indicate sample fault case xiAnd xjBetween similarity, cluster CmAnd CnBetween similarity be two clusters in it is most like
Similarity between case is indicated with following formula (6):
In formula, SijFor cluster CmAnd CnBetween similarity；SIM(xi, xj) it is sample fault case xiAnd xjBetween similarity；xi
For the sample fault case of serial number i；xjFor the sample fault case of serial number j；CmFor the cluster of serial number m；CnFor serial number n
Cluster；
If cluster CrFor cluster CmAnd CnNew cluster made of after merging, then either cluster CaWith CrBetween similarity are as follows:
In formula, SarFor either cluster CaWith CrBetween similarity；CrFor cluster CmAnd CnNew cluster made of merging；
Specific step is as follows for algorithm:
(1) similarity of sample fault case between any two is calculated, the result being calculated is expressed as a similarity matrix,
It is denoted as S (0), at first, each sample fault case self-contained cluster, shown in similarity matrix such as following formula (8):
(2) greatest member for being greater than given threshold η on off-diagonal is found out from similarity matrix S (0), by CmWith CnIt is merged into
One new cluster, is denoted as Cr, i.e. Cr={ Cm, Cn}；
(3) the calculating formula of similarity S by being provided in formula (7)ar=max { Sam, SarCalculate new cluster CrWith other clusters CaBetween
Similarity, m, n row in S (0) and m, n column are merged into a newline and newly arranged, newline made of merging newly arranges and is denoted as cluster
Cr, it is formed by new matrix and is denoted as S (1)；
(4) S (2) are obtained to S (1) (2) (3) two step that repeats the above steps, circuited sequentially, until non-right in similarity matrix S (k)
The greatest member of linea angulata is less than given threshold η；
The fault case retrieval is using Technique Using Both Text case Similarity matching method and the nearest neighbor method based on cloud model as case
The searching algorithm of example calculates the similarity of current failure case Yu historical failure case, by similarity is more than the former of given threshold
Barrier case returns to user, for solving current vehicle failure；
This method by the fault case symptom attribute of fault case, car category, brand, vehicle, automobile type configuration, mileage travelled,
Vehicle date property is purchased as fault case search condition, wherein car category, brand, vehicle, there are levels for automobile type configuration four
The attribute of relationship calculates local similarity as first part, using semantic case Similarity matching method；Remaining fault case
Attribute is then used as second part to calculate similarity using the nearest neighbor method based on cloud model；
The semantic case Similarity matching method is according to node depth weight density monotonically increasing function:
In formula, DepIt (X) is the depth value of nodes X；X is node；α1For the intermediate number of plies, byCapping integer；
And node density weight model:
In formula, Des(c) density value for being node c；Deg(c) out-degree for being node c；
Obtain the local similarity between case attribute:
In formula, S (X, T)OfficeFor case attribute local similarity；ciFor node i；α2For nodes X depth and inverse,
The nearest neighbor method based on cloud model provides the similarity formula between case attribute:
In formula, SIM (X, T)OfficeFor case attribute local similarity；DIST (X, T) is the distance between case attribute；WjFor j attribute
Weighted value；XijFor j-th of attribute value of i-th of history case；TjFor j-th of attribute value of present case；
Similarity between above formula (12) computation attribute is then directly passed through for the fault attribute value of quantitative value；For qualitative mould
Fault attribute is pasted, then the desired value Ex, entropy En, Fog property for calculating qualitative Fog property according to formula (1) (2) (3) (4) are super
The membership clouds expectation curve μ of entropy He and normal distribution, expectation curve represent a qualitative fuzzy fault attribute value, by solving two
The expectation curve of a cloud model intersects the area of lap to indicate the similarity of two qualitative fuzzy fault attributes；And it is final
It sums to calculated qualitative fault attribute similarity and qualitative fuzzy fault attributes similarity, obtains SIM (X, T)Office；Case it
Between final total similarity are as follows:
S(X,T)Always=0.2*S (X, T)Office+0.8*SIM(X,T)Office(13)
In formula, S (X, T)AlwaysFor total similarity of case；
The fault case study module is that new case is added in fault case library, by newly added fault case and historical failure
Case compares, if the historical failure case similarity in newly added fault case and case library is less than the threshold of setting
Newly added fault case, then be saved in fault case library by value, if similarity has been more than the threshold value of setting, illustrates newly to add
The value that the fault case added does not need to save abandons the addition of new fault case；
Step 11: judging after carrying out automobile failure diagnosis by the fault case reasoning based on cloud model, vehicle failure reason
Whether make a definite diagnosis；
Step 12: Process Based automobile failure diagnosis, by the vehicle inspection and maintenance step concluding, summarize to automobile
It carries out fault diagnosis and excludes, including human-computer interaction module and rule-based reasoning module；
The human-computer interaction module of the Process Based provides fault diagnosis and maintenance Step Information for user；
The rule-based reasoning module connects human-computer interaction module, including rule base and forward reasoning unit；
The rule base stores the institute from practical experience by application failure maintenance step table and trouble hunting rule list respectively
The specific rules of acquisition；
The troubleshooting step table includes number of steps, step title, step details, remark information；
The trouble hunting rule list includes number of steps, inspection result, in next step number, remark information；
The forward reasoning unit uses WetherYes or No format, judges whether current detection condition is true, if at
It is vertical, then enter next detecting step corresponding with Yes, if not, then enter another detecting step corresponding with No, according to
This circulation, until completing current automobile failure diagnosis；
Step 13: maintenance auxiliary: making a definite diagnosis vapour by parsing error code or the fault case reasoning fault diagnosis based on cloud model
It after the reason of vehicle breaks down, is assisted by using maintenance, step, maintenance tool, maintenance price, maintenance time information will be repaired
User is returned to, provides the vehicle failure maintenance flow of standard for maintenance personal, provides the auto repair price of standard for client,
Including human-computer interaction module, Maintenance Cases reasoning module and Maintenance Cases study module；
The human-computer interaction module of the maintenance auxiliary connects Maintenance Cases reasoning module, realizes user to maintenance retrieval information
Typing；
The Maintenance Cases reasoning module includes the expression of Maintenance Cases and the retrieval of Maintenance Cases；
The expression of the Maintenance Cases indicates auto repair case with a four-tuple:
C=<I, G, S, L>(14)
In formula, I is Maintenance Cases unique identifier；G is maintenance tool, G={ G1, G2..., Gn}；S is maintenance solution；L
For the aid illustration information of Maintenance Cases, L={ L1, L2..., Ln}；
The described Maintenance Cases retrieval by Maintenance Cases attribute car category, brand, vehicle, automobile type configuration, maintenance mode,
Car fare lattice, purchase vehicle date are purchased as Maintenance Cases search condition, wherein car category, brand, vehicle, automobile type configuration four presence
The semantic case Similarity matching method that the attribute of hierarchical relationship uses fault case search method as above to use as first part
Calculate local similarity；Remaining Maintenance Cases attribute is then used as second part to calculate using nearest neighbor method shown in above formula (12)
Similarity between Maintenance Cases；Most like Maintenance Cases are retrieved from Maintenance Cases library, by maintenance tool needed for it, dimension
Repair step, maintenance price, maintenance time return to maintenance personal or client, provide reference for it；
The maintenance mode attribute is divided to two kinds of values, that is, replaces and repair；According to Experts consultation method in advance by maintenance mode attribute
When to replace, it is set as 0.5, to be set as 0 when maintenance；
The Maintenance Cases study module is stored part useful in new Maintenance Cases to dimension using the mode of learning of increment type
It repairs in case library, enriches itself case library.
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| CN113128687A (en) * | 2021-03-25 | 2021-07-16 | 北京博华信智科技股份有限公司 | Fault diagnosis expert system for escalator |
| CN114265384A (en) * | 2021-11-22 | 2022-04-01 | 阿尔特汽车技术股份有限公司 | Vehicle fault information processing method and system |
| CN114296105B (en) * | 2021-12-27 | 2024-06-14 | 中国第一汽车股份有限公司 | Method, device, equipment and storage medium for determining positioning fault cause |
| CN114636890B (en) * | 2022-01-29 | 2023-10-10 | 国网河北省电力有限公司邯郸供电分公司 | Case-based reasoning fault positioning method and system, storage medium and electronic equipment |
| CN114510007A (en) * | 2022-02-17 | 2022-05-17 | 重庆朗维机电技术有限公司 | Intelligent integrated fault diagnosis method and device for automobile production line |
| CN118466474B (en) * | 2024-07-15 | 2024-09-13 | 深圳联鹏高远智能科技有限公司 | Control method and system for automobile maintenance platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1703449A1 (en) * | 2005-03-18 | 2006-09-20 | BRITISH TELECOMMUNICATIONS public limited company | Fault diagnostics |
| CN103091112A (en) * | 2013-01-31 | 2013-05-08 | 林惠堂 | Method and device of car emission fault detection and diagnosis based on fuzzy reasoning and self-learning |
| CN103115779A (en) * | 2012-10-24 | 2013-05-22 | 中国电力科学研究院 | Electric automobile on-line state monitoring system |
| CN103135014A (en) * | 2012-12-14 | 2013-06-05 | 西安电子科技大学 | Transformer fault diagnosis method based on case-based reasoning |
| CN103455025A (en) * | 2013-08-15 | 2013-12-18 | 重庆邮电大学 | Automobile fault diagnosis system based on Android platform |
-
2017
- 2017-05-26 CN CN201710369129.3A patent/CN107168285B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1703449A1 (en) * | 2005-03-18 | 2006-09-20 | BRITISH TELECOMMUNICATIONS public limited company | Fault diagnostics |
| CN103115779A (en) * | 2012-10-24 | 2013-05-22 | 中国电力科学研究院 | Electric automobile on-line state monitoring system |
| CN103135014A (en) * | 2012-12-14 | 2013-06-05 | 西安电子科技大学 | Transformer fault diagnosis method based on case-based reasoning |
| CN103091112A (en) * | 2013-01-31 | 2013-05-08 | 林惠堂 | Method and device of car emission fault detection and diagnosis based on fuzzy reasoning and self-learning |
| CN103455025A (en) * | 2013-08-15 | 2013-12-18 | 重庆邮电大学 | Automobile fault diagnosis system based on Android platform |
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